Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations696
Missing cells747
Missing cells (%)5.1%
Total size in memory114.3 KiB
Average record size in memory168.2 B

Variable types

Text12
Numeric6
DateTime1
Unsupported2

Alerts

Altura has 567 (81.5%) missing values Missing
Cruce has 171 (24.6%) missing values Missing
Dirección normalizada has 8 (1.1%) missing values Missing
Id has unique values Unique
Hora is an unsupported type, check if it needs cleaning or further analysis Unsupported
HH is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2024-10-22 23:46:20.875017
Analysis finished2024-10-22 23:46:21.458630
Duration0.58 seconds
Software versionydata-profiling vv4.11.0
Download configurationconfig.json

Variables

Id
Text

Unique 

Distinct696
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
2024-10-22T20:46:22.057983image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters6264
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique696 ?
Unique (%)100.0%

Sample

1st row2016-0001
2nd row2016-0002
3rd row2016-0003
4th row2016-0004
5th row2016-0005
ValueCountFrequency (%)
2016-0001 1
 
0.1%
2016-0013 1
 
0.1%
2016-0015 1
 
0.1%
2016-0003 1
 
0.1%
2016-0004 1
 
0.1%
2016-0005 1
 
0.1%
2016-0008 1
 
0.1%
2016-0009 1
 
0.1%
2016-0010 1
 
0.1%
2016-0012 1
 
0.1%
Other values (686) 686
98.6%
2024-10-22T20:46:23.106942image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2143
34.2%
2 1020
16.3%
1 941
15.0%
- 696
 
11.1%
6 275
 
4.4%
7 265
 
4.2%
8 262
 
4.2%
9 215
 
3.4%
3 154
 
2.5%
5 149
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6264
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2143
34.2%
2 1020
16.3%
1 941
15.0%
- 696
 
11.1%
6 275
 
4.4%
7 265
 
4.2%
8 262
 
4.2%
9 215
 
3.4%
3 154
 
2.5%
5 149
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6264
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2143
34.2%
2 1020
16.3%
1 941
15.0%
- 696
 
11.1%
6 275
 
4.4%
7 265
 
4.2%
8 262
 
4.2%
9 215
 
3.4%
3 154
 
2.5%
5 149
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6264
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2143
34.2%
2 1020
16.3%
1 941
15.0%
- 696
 
11.1%
6 275
 
4.4%
7 265
 
4.2%
8 262
 
4.2%
9 215
 
3.4%
3 154
 
2.5%
5 149
 
2.4%

Cantidad de victimas
Real number (ℝ)

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.030172414
Minimum1
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-10-22T20:46:23.306420image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum3
Range2
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1793929785
Coefficient of variation (CV)0.1741387908
Kurtosis41.64101944
Mean1.030172414
Median Absolute Deviation (MAD)0
Skewness6.237772368
Sum717
Variance0.03218184073
MonotonicityNot monotonic
2024-10-22T20:46:23.489814image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
1 676
97.1%
2 19
 
2.7%
3 1
 
0.1%
ValueCountFrequency (%)
1 676
97.1%
2 19
 
2.7%
3 1
 
0.1%
ValueCountFrequency (%)
3 1
 
0.1%
2 19
 
2.7%
1 676
97.1%

Fecha
Date

Distinct598
Distinct (%)85.9%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
Minimum2016-01-01 00:00:00
Maximum2021-12-30 00:00:00
2024-10-22T20:46:23.722926image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-10-22T20:46:23.994036image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Año
Real number (ℝ)

Distinct6
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.188218
Minimum2016
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-10-22T20:46:24.205414image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum2016
5-th percentile2016
Q12017
median2018
Q32020
95-th percentile2021
Maximum2021
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.683753682
Coefficient of variation (CV)0.0008342897192
Kurtosis-1.114955943
Mean2018.188218
Median Absolute Deviation (MAD)1
Skewness0.2859078718
Sum1404659
Variance2.835026462
MonotonicityIncreasing
2024-10-22T20:46:24.373633image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2016 144
20.7%
2018 143
20.5%
2017 131
18.8%
2019 103
14.8%
2021 97
13.9%
2020 78
11.2%
ValueCountFrequency (%)
2016 144
20.7%
2017 131
18.8%
2018 143
20.5%
2019 103
14.8%
2020 78
11.2%
ValueCountFrequency (%)
2021 97
13.9%
2020 78
11.2%
2019 103
14.8%
2018 143
20.5%
2017 131
18.8%

Mes
Real number (ℝ)

Distinct12
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.692528736
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-10-22T20:46:24.555244image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.57130876
Coefficient of variation (CV)0.5336262123
Kurtosis-1.251306282
Mean6.692528736
Median Absolute Deviation (MAD)3
Skewness-0.04724435483
Sum4658
Variance12.75424626
MonotonicityNot monotonic
2024-10-22T20:46:24.738655image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
12 78
11.2%
11 67
9.6%
8 65
9.3%
1 62
8.9%
5 60
8.6%
6 58
8.3%
2 56
8.0%
3 51
7.3%
7 51
7.3%
10 51
7.3%
Other values (2) 97
13.9%
ValueCountFrequency (%)
1 62
8.9%
2 56
8.0%
3 51
7.3%
4 50
7.2%
5 60
8.6%
ValueCountFrequency (%)
12 78
11.2%
11 67
9.6%
10 51
7.3%
9 47
6.8%
8 65
9.3%

Día
Real number (ℝ)

Distinct31
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.93678161
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-10-22T20:46:24.921744image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q19
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.639645809
Coefficient of variation (CV)0.5421198596
Kurtosis-1.14960695
Mean15.93678161
Median Absolute Deviation (MAD)7
Skewness-0.03236210607
Sum11092
Variance74.6434797
MonotonicityNot monotonic
2024-10-22T20:46:25.121401image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
20 31
 
4.5%
17 30
 
4.3%
3 27
 
3.9%
11 27
 
3.9%
27 27
 
3.9%
12 26
 
3.7%
14 26
 
3.7%
28 25
 
3.6%
10 25
 
3.6%
9 25
 
3.6%
Other values (21) 427
61.4%
ValueCountFrequency (%)
1 18
2.6%
2 22
3.2%
3 27
3.9%
4 23
3.3%
5 18
2.6%
ValueCountFrequency (%)
31 13
1.9%
30 16
2.3%
29 22
3.2%
28 25
3.6%
27 27
3.9%

Hora
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size5.6 KiB

HH
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size5.6 KiB
Distinct683
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
2024-10-22T20:46:25.653909image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length85
Median length52
Mean length28.86350575
Min length2

Characters and Unicode

Total characters20089
Distinct characters77
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique672 ?
Unique (%)96.6%

Sample

1st rowAV PIEDRA BUENA Y AV FERNANDEZ DE LA CRUZ
2nd rowAV GRAL PAZ Y AV DE LOS CORRALES
3rd rowAV ENTRE RIOS 2034
4th rowAV LARRAZABAL Y GRAL VILLEGAS CONRADO
5th rowAV SAN JUAN Y PRESIDENTE LUIS SAENZ PEÑA
ValueCountFrequency (%)
av 617
 
16.4%
y 526
 
14.0%
de 121
 
3.2%
gral 97
 
2.6%
paz 70
 
1.9%
juan 48
 
1.3%
la 42
 
1.1%
au 40
 
1.1%
del 34
 
0.9%
san 31
 
0.8%
Other values (803) 2140
56.8%
2024-10-22T20:46:26.420035image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3086
 
15.4%
A 2315
 
11.5%
E 1077
 
5.4%
R 1064
 
5.3%
O 945
 
4.7%
L 756
 
3.8%
I 739
 
3.7%
N 699
 
3.5%
. 680
 
3.4%
V 658
 
3.3%
Other values (67) 8070
40.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20089
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3086
 
15.4%
A 2315
 
11.5%
E 1077
 
5.4%
R 1064
 
5.3%
O 945
 
4.7%
L 756
 
3.8%
I 739
 
3.7%
N 699
 
3.5%
. 680
 
3.4%
V 658
 
3.3%
Other values (67) 8070
40.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20089
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3086
 
15.4%
A 2315
 
11.5%
E 1077
 
5.4%
R 1064
 
5.3%
O 945
 
4.7%
L 756
 
3.8%
I 739
 
3.7%
N 699
 
3.5%
. 680
 
3.4%
V 658
 
3.3%
Other values (67) 8070
40.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20089
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3086
 
15.4%
A 2315
 
11.5%
E 1077
 
5.4%
R 1064
 
5.3%
O 945
 
4.7%
L 756
 
3.8%
I 739
 
3.7%
N 699
 
3.5%
. 680
 
3.4%
V 658
 
3.3%
Other values (67) 8070
40.2%
Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
2024-10-22T20:46:26.669715image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length9
Median length7
Mean length6.892241379
Min length5

Characters and Unicode

Total characters4797
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAVENIDA
2nd rowGRAL PAZ
3rd rowAVENIDA
4th rowAVENIDA
5th rowAVENIDA
ValueCountFrequency (%)
avenida 429
56.4%
calle 136
 
17.9%
autopista 66
 
8.7%
gral 65
 
8.5%
paz 65
 
8.5%
2024-10-22T20:46:27.119446image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 1256
26.2%
E 565
11.8%
I 495
 
10.3%
N 429
 
8.9%
D 429
 
8.9%
V 429
 
8.9%
L 337
 
7.0%
C 136
 
2.8%
T 132
 
2.8%
P 131
 
2.7%
Other values (7) 458
 
9.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4797
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 1256
26.2%
E 565
11.8%
I 495
 
10.3%
N 429
 
8.9%
D 429
 
8.9%
V 429
 
8.9%
L 337
 
7.0%
C 136
 
2.8%
T 132
 
2.8%
P 131
 
2.7%
Other values (7) 458
 
9.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4797
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 1256
26.2%
E 565
11.8%
I 495
 
10.3%
N 429
 
8.9%
D 429
 
8.9%
V 429
 
8.9%
L 337
 
7.0%
C 136
 
2.8%
T 132
 
2.8%
P 131
 
2.7%
Other values (7) 458
 
9.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4797
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 1256
26.2%
E 565
11.8%
I 495
 
10.3%
N 429
 
8.9%
D 429
 
8.9%
V 429
 
8.9%
L 337
 
7.0%
C 136
 
2.8%
T 132
 
2.8%
P 131
 
2.7%
Other values (7) 458
 
9.5%

Calle
Text

Distinct279
Distinct (%)40.1%
Missing1
Missing (%)0.1%
Memory size5.6 KiB
2024-10-22T20:46:27.569097image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length42
Median length32
Mean length16.05467626
Min length4

Characters and Unicode

Total characters11158
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique171 ?
Unique (%)24.6%

Sample

1st rowPIEDRA BUENA AV.
2nd rowPAZ, GRAL. AV.
3rd rowENTRE RIOS AV.
4th rowLARRAZABAL AV.
5th rowSAN JUAN AV.
ValueCountFrequency (%)
av 419
 
22.3%
gral 86
 
4.6%
de 72
 
3.8%
paz 58
 
3.1%
autopista 53
 
2.8%
juan 38
 
2.0%
del 24
 
1.3%
la 23
 
1.2%
moreno 23
 
1.2%
san 22
 
1.2%
Other values (375) 1063
56.5%
2024-10-22T20:46:28.335510image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 1622
14.5%
1186
 
10.6%
E 798
 
7.2%
R 795
 
7.1%
O 704
 
6.3%
I 590
 
5.3%
. 586
 
5.3%
V 527
 
4.7%
L 518
 
4.6%
N 503
 
4.5%
Other values (28) 3329
29.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11158
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 1622
14.5%
1186
 
10.6%
E 798
 
7.2%
R 795
 
7.1%
O 704
 
6.3%
I 590
 
5.3%
. 586
 
5.3%
V 527
 
4.7%
L 518
 
4.6%
N 503
 
4.5%
Other values (28) 3329
29.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11158
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 1622
14.5%
1186
 
10.6%
E 798
 
7.2%
R 795
 
7.1%
O 704
 
6.3%
I 590
 
5.3%
. 586
 
5.3%
V 527
 
4.7%
L 518
 
4.6%
N 503
 
4.5%
Other values (28) 3329
29.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11158
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 1622
14.5%
1186
 
10.6%
E 798
 
7.2%
R 795
 
7.1%
O 704
 
6.3%
I 590
 
5.3%
. 586
 
5.3%
V 527
 
4.7%
L 518
 
4.6%
N 503
 
4.5%
Other values (28) 3329
29.8%

Altura
Real number (ℝ)

Missing 

Distinct126
Distinct (%)97.7%
Missing567
Missing (%)81.5%
Infinite0
Infinite (%)0.0%
Mean3336.635659
Minimum30
Maximum16080
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-10-22T20:46:28.551036image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile365
Q11359
median2551
Q34500
95-th percentile9388.4
Maximum16080
Range16050
Interquartile range (IQR)3141

Descriptive statistics

Standard deviation3060.641793
Coefficient of variation (CV)0.9172837871
Kurtosis5.698668231
Mean3336.635659
Median Absolute Deviation (MAD)1433
Skewness2.159436524
Sum430426
Variance9367528.187
MonotonicityNot monotonic
2024-10-22T20:46:28.950849image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
365 2
 
0.3%
4300 2
 
0.3%
901 2
 
0.3%
4650 1
 
0.1%
16080 1
 
0.1%
466 1
 
0.1%
2250 1
 
0.1%
5455 1
 
0.1%
6700 1
 
0.1%
2428 1
 
0.1%
Other values (116) 116
 
16.7%
(Missing) 567
81.5%
ValueCountFrequency (%)
30 1
0.1%
133 1
0.1%
150 1
0.1%
156 1
0.1%
300 1
0.1%
ValueCountFrequency (%)
16080 1
0.1%
15200 1
0.1%
14800 1
0.1%
14723 1
0.1%
11200 1
0.1%

Cruce
Text

Missing 

Distinct317
Distinct (%)60.4%
Missing171
Missing (%)24.6%
Memory size5.6 KiB
2024-10-22T20:46:29.500381image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length42
Median length30
Mean length13.93333333
Min length3

Characters and Unicode

Total characters7315
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique208 ?
Unique (%)39.6%

Sample

1st rowFERNANDEZ DE LA CRUZ, F., GRAL. AV.
2nd rowDE LOS CORRALES AV.
3rd rowVILLEGAS, CONRADO, GRAL.
4th rowSAENZ PE?A, LUIS, PRES.
5th rowESCALADA AV.
ValueCountFrequency (%)
av 216
 
18.0%
de 41
 
3.4%
gral 28
 
2.3%
la 16
 
1.3%
dr 15
 
1.3%
paz 14
 
1.2%
juan 13
 
1.1%
del 11
 
0.9%
cnel 11
 
0.9%
luis 9
 
0.8%
Other values (431) 823
68.8%
2024-10-22T20:46:30.299511image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 1079
14.8%
672
 
9.2%
E 581
 
7.9%
R 546
 
7.5%
O 471
 
6.4%
N 395
 
5.4%
L 374
 
5.1%
I 365
 
5.0%
. 319
 
4.4%
V 315
 
4.3%
Other values (30) 2198
30.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7315
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 1079
14.8%
672
 
9.2%
E 581
 
7.9%
R 546
 
7.5%
O 471
 
6.4%
N 395
 
5.4%
L 374
 
5.1%
I 365
 
5.0%
. 319
 
4.4%
V 315
 
4.3%
Other values (30) 2198
30.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7315
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 1079
14.8%
672
 
9.2%
E 581
 
7.9%
R 546
 
7.5%
O 471
 
6.4%
N 395
 
5.4%
L 374
 
5.1%
I 365
 
5.0%
. 319
 
4.4%
V 315
 
4.3%
Other values (30) 2198
30.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7315
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 1079
14.8%
672
 
9.2%
E 581
 
7.9%
R 546
 
7.5%
O 471
 
6.4%
N 395
 
5.4%
L 374
 
5.1%
I 365
 
5.0%
. 319
 
4.4%
V 315
 
4.3%
Other values (30) 2198
30.0%
Distinct635
Distinct (%)92.3%
Missing8
Missing (%)1.1%
Memory size5.6 KiB
2024-10-22T20:46:30.887031image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length75
Median length51
Mean length30.40116279
Min length8

Characters and Unicode

Total characters20916
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique594 ?
Unique (%)86.3%

Sample

1st rowPIEDRA BUENA AV. y FERNANDEZ DE LA CRUZ, F., GRAL. AV.
2nd rowPAZ, GRAL. AV. y DE LOS CORRALES AV.
3rd rowENTRE RIOS AV. 2034
4th rowLARRAZABAL AV. y VILLEGAS, CONRADO, GRAL.
5th rowSAN JUAN AV. y SAENZ PEÑA, LUIS, PRES.
ValueCountFrequency (%)
av 642
 
17.0%
y 537
 
14.2%
de 118
 
3.1%
gral 114
 
3.0%
paz 72
 
1.9%
autopista 54
 
1.4%
juan 51
 
1.3%
la 38
 
1.0%
del 35
 
0.9%
san 31
 
0.8%
Other values (736) 2094
55.3%
2024-10-22T20:46:31.731220image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3100
14.8%
A 2719
13.0%
E 1393
 
6.7%
R 1355
 
6.5%
O 1172
 
5.6%
I 958
 
4.6%
. 923
 
4.4%
N 908
 
4.3%
L 889
 
4.3%
V 854
 
4.1%
Other values (40) 6645
31.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20916
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3100
14.8%
A 2719
13.0%
E 1393
 
6.7%
R 1355
 
6.5%
O 1172
 
5.6%
I 958
 
4.6%
. 923
 
4.4%
N 908
 
4.3%
L 889
 
4.3%
V 854
 
4.1%
Other values (40) 6645
31.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20916
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3100
14.8%
A 2719
13.0%
E 1393
 
6.7%
R 1355
 
6.5%
O 1172
 
5.6%
I 958
 
4.6%
. 923
 
4.4%
N 908
 
4.3%
L 889
 
4.3%
V 854
 
4.1%
Other values (40) 6645
31.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20916
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3100
14.8%
A 2719
13.0%
E 1393
 
6.7%
R 1355
 
6.5%
O 1172
 
5.6%
I 958
 
4.6%
. 923
 
4.4%
N 908
 
4.3%
L 889
 
4.3%
V 854
 
4.1%
Other values (40) 6645
31.8%

Comuna
Real number (ℝ)

Distinct16
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.425287356
Minimum0
Maximum15
Zeros2
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-10-22T20:46:31.914299image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median8
Q311
95-th percentile15
Maximum15
Range15
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.387050097
Coefficient of variation (CV)0.5908256322
Kurtosis-1.125472763
Mean7.425287356
Median Absolute Deviation (MAD)4
Skewness0.09875376388
Sum5168
Variance19.24620855
MonotonicityNot monotonic
2024-10-22T20:46:32.097601image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1 90
12.9%
4 76
10.9%
9 73
10.5%
8 65
9.3%
7 60
8.6%
3 45
 
6.5%
15 44
 
6.3%
13 40
 
5.7%
12 37
 
5.3%
14 35
 
5.0%
Other values (6) 131
18.8%
ValueCountFrequency (%)
0 2
 
0.3%
1 90
12.9%
2 25
 
3.6%
3 45
6.5%
4 76
10.9%
ValueCountFrequency (%)
15 44
6.3%
14 35
5.0%
13 40
5.7%
12 37
5.3%
11 32
4.6%

xy
Text

Distinct606
Distinct (%)87.1%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
2024-10-22T20:46:32.466134image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length39
Median length38
Mean length37.66522989
Min length11

Characters and Unicode

Total characters26215
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique550 ?
Unique (%)79.0%

Sample

1st rowPoint (98896.78238426 93532.43437792)
2nd rowPoint (95832.05571093 95505.41641999)
3rd rowPoint (106684.29090040 99706.57687843)
4th rowPoint (99840.65224780 94269.16534422)
5th rowPoint (106980.32827929 100752.16915795)
ValueCountFrequency (%)
point 696
33.3%
28
 
1.3%
101721.59002217 5
 
0.2%
93844.25656649 5
 
0.2%
96563.66494817 4
 
0.2%
108815.73881056 4
 
0.2%
99620.34936816 4
 
0.2%
110483.29286598 4
 
0.2%
95832.05571093 4
 
0.2%
95505.41641999 4
 
0.2%
Other values (1202) 1330
63.7%
2024-10-22T20:46:33.062997image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 2457
 
9.4%
0 2454
 
9.4%
9 2196
 
8.4%
8 1717
 
6.5%
4 1670
 
6.4%
7 1655
 
6.3%
5 1639
 
6.3%
2 1608
 
6.1%
3 1589
 
6.1%
6 1574
 
6.0%
Other values (9) 7656
29.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26215
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 2457
 
9.4%
0 2454
 
9.4%
9 2196
 
8.4%
8 1717
 
6.5%
4 1670
 
6.4%
7 1655
 
6.3%
5 1639
 
6.3%
2 1608
 
6.1%
3 1589
 
6.1%
6 1574
 
6.0%
Other values (9) 7656
29.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26215
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 2457
 
9.4%
0 2454
 
9.4%
9 2196
 
8.4%
8 1717
 
6.5%
4 1670
 
6.4%
7 1655
 
6.3%
5 1639
 
6.3%
2 1608
 
6.1%
3 1589
 
6.1%
6 1574
 
6.0%
Other values (9) 7656
29.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26215
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 2457
 
9.4%
0 2454
 
9.4%
9 2196
 
8.4%
8 1717
 
6.5%
4 1670
 
6.4%
7 1655
 
6.3%
5 1639
 
6.3%
2 1608
 
6.1%
3 1589
 
6.1%
6 1574
 
6.0%
Other values (9) 7656
29.2%

Pos x
Text

Distinct605
Distinct (%)86.9%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
2024-10-22T20:46:33.479576image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length18
Median length12
Mean length11.82758621
Min length1

Characters and Unicode

Total characters8232
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique546 ?
Unique (%)78.4%

Sample

1st row-58.47533969
2nd row-58.50877521
3rd row-58.39040293
4th row-58.46503904
5th row-58.38718297
ValueCountFrequency (%)
12
 
1.7%
58.44451316 5
 
0.7%
58.50073810 4
 
0.6%
58.46743471 4
 
0.6%
58.50877521 4
 
0.6%
58.48727942 3
 
0.4%
58.49491054 3
 
0.4%
58.47976785 3
 
0.4%
58.38526125 3
 
0.4%
58.37533517 3
 
0.4%
Other values (595) 652
93.7%
2024-10-22T20:46:34.094794image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 1213
14.7%
8 1126
13.7%
4 911
11.1%
. 696
8.5%
- 684
8.3%
3 571
6.9%
1 539
6.5%
2 517
6.3%
6 512
6.2%
9 509
6.2%
Other values (2) 954
11.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8232
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 1213
14.7%
8 1126
13.7%
4 911
11.1%
. 696
8.5%
- 684
8.3%
3 571
6.9%
1 539
6.5%
2 517
6.3%
6 512
6.2%
9 509
6.2%
Other values (2) 954
11.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8232
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 1213
14.7%
8 1126
13.7%
4 911
11.1%
. 696
8.5%
- 684
8.3%
3 571
6.9%
1 539
6.5%
2 517
6.3%
6 512
6.2%
9 509
6.2%
Other values (2) 954
11.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8232
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 1213
14.7%
8 1126
13.7%
4 911
11.1%
. 696
8.5%
- 684
8.3%
3 571
6.9%
1 539
6.5%
2 517
6.3%
6 512
6.2%
9 509
6.2%
Other values (2) 954
11.6%

Pos y
Text

Distinct605
Distinct (%)86.9%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
2024-10-22T20:46:34.478408image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length18
Median length12
Mean length11.82758621
Min length1

Characters and Unicode

Total characters8232
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique546 ?
Unique (%)78.4%

Sample

1st row-34.68757022
2nd row-34.66977709
3rd row-34.63189362
4th row-34.68092974
5th row-34.62246630
ValueCountFrequency (%)
12
 
1.7%
34.68475866 5
 
0.7%
34.54979510 4
 
0.6%
34.53476874 4
 
0.6%
34.66977709 4
 
0.6%
34.63652467 3
 
0.4%
34.54795581 3
 
0.4%
34.69153196 3
 
0.4%
34.57805810 3
 
0.4%
34.59276462 3
 
0.4%
Other values (595) 652
93.7%
2024-10-22T20:46:35.094761image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 1172
14.2%
3 1147
13.9%
6 929
11.3%
5 725
8.8%
. 696
8.5%
- 684
8.3%
7 531
6.5%
9 502
6.1%
2 479
5.8%
1 463
 
5.6%
Other values (2) 904
11.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8232
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 1172
14.2%
3 1147
13.9%
6 929
11.3%
5 725
8.8%
. 696
8.5%
- 684
8.3%
7 531
6.5%
9 502
6.1%
2 479
5.8%
1 463
 
5.6%
Other values (2) 904
11.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8232
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 1172
14.2%
3 1147
13.9%
6 929
11.3%
5 725
8.8%
. 696
8.5%
- 684
8.3%
7 531
6.5%
9 502
6.1%
2 479
5.8%
1 463
 
5.6%
Other values (2) 904
11.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8232
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 1172
14.2%
3 1147
13.9%
6 929
11.3%
5 725
8.8%
. 696
8.5%
- 684
8.3%
7 531
6.5%
9 502
6.1%
2 479
5.8%
1 463
 
5.6%
Other values (2) 904
11.0%
Distinct41
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
2024-10-22T20:46:35.427695image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length19
Median length18
Mean length12.24856322
Min length5

Characters and Unicode

Total characters8525
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)1.9%

Sample

1st rowMOTO-AUTO
2nd rowAUTO-PASAJEROS
3rd rowMOTO-AUTO
4th rowMOTO-SD
5th rowMOTO-PASAJEROS
ValueCountFrequency (%)
peaton-pasajeros 105
13.8%
moto-auto 83
10.9%
moto-cargas 78
10.3%
peaton-auto 77
10.1%
fijo 63
 
8.3%
moto-pasajeros 46
 
6.1%
moto-objeto 40
 
5.3%
peaton-cargas 38
 
5.0%
auto-auto 31
 
4.1%
peaton-moto 30
 
4.0%
Other values (32) 168
22.1%
2024-10-22T20:46:35.943433image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
O 1612
18.9%
A 1241
14.6%
T 1008
11.8%
- 679
8.0%
E 554
 
6.5%
S 541
 
6.3%
P 455
 
5.3%
M 367
 
4.3%
R 335
 
3.9%
J 304
 
3.6%
Other values (12) 1429
16.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8525
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 1612
18.9%
A 1241
14.6%
T 1008
11.8%
- 679
8.0%
E 554
 
6.5%
S 541
 
6.3%
P 455
 
5.3%
M 367
 
4.3%
R 335
 
3.9%
J 304
 
3.6%
Other values (12) 1429
16.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8525
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 1612
18.9%
A 1241
14.6%
T 1008
11.8%
- 679
8.0%
E 554
 
6.5%
S 541
 
6.3%
P 455
 
5.3%
M 367
 
4.3%
R 335
 
3.9%
J 304
 
3.6%
Other values (12) 1429
16.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8525
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 1612
18.9%
A 1241
14.6%
T 1008
11.8%
- 679
8.0%
E 554
 
6.5%
S 541
 
6.3%
P 455
 
5.3%
M 367
 
4.3%
R 335
 
3.9%
J 304
 
3.6%
Other values (12) 1429
16.8%
Distinct10
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
2024-10-22T20:46:36.193703image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length11
Median length4
Mean length5.020114943
Min length2

Characters and Unicode

Total characters3494
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.3%

Sample

1st rowMOTO
2nd rowAUTO
3rd rowMOTO
4th rowMOTO
5th rowMOTO
ValueCountFrequency (%)
moto 295
42.3%
peaton 264
37.9%
auto 83
 
11.9%
bicicleta 29
 
4.2%
sd 9
 
1.3%
cargas 7
 
1.0%
pasajeros 5
 
0.7%
movil 2
 
0.3%
objeto 1
 
0.1%
fijo 1
 
0.1%
2024-10-22T20:46:36.626432image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
O 950
27.2%
T 674
19.3%
A 401
11.5%
E 300
 
8.6%
M 298
 
8.5%
P 270
 
7.7%
N 265
 
7.6%
U 83
 
2.4%
C 65
 
1.9%
I 61
 
1.7%
Other values (11) 127
 
3.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3494
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 950
27.2%
T 674
19.3%
A 401
11.5%
E 300
 
8.6%
M 298
 
8.5%
P 270
 
7.7%
N 265
 
7.6%
U 83
 
2.4%
C 65
 
1.9%
I 61
 
1.7%
Other values (11) 127
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3494
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 950
27.2%
T 674
19.3%
A 401
11.5%
E 300
 
8.6%
M 298
 
8.5%
P 270
 
7.7%
N 265
 
7.6%
U 83
 
2.4%
C 65
 
1.9%
I 61
 
1.7%
Other values (11) 127
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3494
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 950
27.2%
T 674
19.3%
A 401
11.5%
E 300
 
8.6%
M 298
 
8.5%
P 270
 
7.7%
N 265
 
7.6%
U 83
 
2.4%
C 65
 
1.9%
I 61
 
1.7%
Other values (11) 127
 
3.6%
Distinct10
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
2024-10-22T20:46:36.959445image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length11
Median length9
Mean length6.367816092
Min length2

Characters and Unicode

Total characters4432
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowAUTO
2nd rowPASAJEROS
3rd rowAUTO
4th rowSD
5th rowPASAJEROS
ValueCountFrequency (%)
auto 204
26.9%
pasajeros 173
22.8%
cargas 146
19.3%
objeto 62
 
8.2%
fijo 62
 
8.2%
moto 57
 
7.5%
sd 23
 
3.0%
multiple 17
 
2.2%
bicicleta 7
 
0.9%
otro 6
 
0.8%
2024-10-22T20:46:37.392360image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 849
19.2%
O 689
15.5%
S 515
11.6%
T 354
8.0%
R 326
 
7.4%
J 297
 
6.7%
E 260
 
5.9%
U 221
 
5.0%
P 190
 
4.3%
C 160
 
3.6%
Other values (9) 571
12.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4432
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 849
19.2%
O 689
15.5%
S 515
11.6%
T 354
8.0%
R 326
 
7.4%
J 297
 
6.7%
E 260
 
5.9%
U 221
 
5.0%
P 190
 
4.3%
C 160
 
3.6%
Other values (9) 571
12.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4432
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 849
19.2%
O 689
15.5%
S 515
11.6%
T 354
8.0%
R 326
 
7.4%
J 297
 
6.7%
E 260
 
5.9%
U 221
 
5.0%
P 190
 
4.3%
C 160
 
3.6%
Other values (9) 571
12.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4432
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 849
19.2%
O 689
15.5%
S 515
11.6%
T 354
8.0%
R 326
 
7.4%
J 297
 
6.7%
E 260
 
5.9%
U 221
 
5.0%
P 190
 
4.3%
C 160
 
3.6%
Other values (9) 571
12.9%